Partitioned echo canceler utilizing decimation echo location

ABSTRACT

The present invention includes an adaptive filter (40) for use within an echo canceler. The adaptive filter operates in a first mode for performing Decimation Echo Location (DEL). During the DEL mode, high energy regions occurring within the echo impulse response are identified over an extended time range through signal decimation. The adaptive filter then operates in a second mode for performing Partitioned Echo Cancellation (PEC). During the PEC mode, filter coefficients not associated with these high energy regions are modeled as zero while the identified high energy region filter coefficients of the impulse response are partitioned and convolution of the reference signal, x(n), with the impulse response signal, h(n), is accomplished by performing convolution on only those samples in the reference signal memory block (50) that correspond to respective high energy region filter coefficients in the adaptive filter block (56). Accordingly, the adaptive filter of the present invention extends the tail length time range of conventional adaptive filters while not increasing the number of required filter coefficient taps and substantially reduces the number of convolution multiply accumulate operations and coefficient update calculations required for performing echo cancellation over the extended time range.

BACKGROUND OF THE INVENTION

This invention relates to echo cancelers and, in particular, to apartitioned echo canceler that utilizes decimation echo location forextending the tail length of an echo canceler without increasing thereal time complexity and without significantly increasing convergencetime of the echo canceler.

Echo cancelers are utilized in a variety of applications for cancelingthe effects of a reflected signal that results from the undesiredcoupling of a device's output signal (electrical or acoustic) back toits input signal path. Functionally, an echo canceler receives both theoriginal reference signal and the reflected signal and attempts toapproximate what the reflected signal will be from the originalreference signal. The echo canceler then subtracts this approximatedreflected signal from the actual reflected signal to obtain an errorsignal. Ideally, this error signal is zero if the echo canceler hassuccessfully approximated the reflected signal and no reflected signalwill be returned back to the transmitting source as desired.

Referring to FIG. 1, a configuration of a typical placement of an echocanceler in a telephony system whereby reference signal x(n) istransmitted to hybrid 12 and reflected signal r(n) is generated as aresult of an impedance mismatch of hybrid 12. FIG. 1 illustrates atypical telephony system whereby a four-wire loop is converted to atwo-wire line through hybrid 12. For purposes of illustration only,assume that a reference signal x(n) is transmitted from a transmittingsource onto a four-wire loop and is received by hybrid 12. The hybrid 12attempts to apply the signal onto the two-wire line and prohibit thetransmitted signal from returning (as echo) on the receive path of thefour-wire loop. However, in order for hybrid 12 to eliminate the effectsof echo, the precise impedance of the two-wire line must be known.However, the impedance of a two-wire line can vary in different systemsthereby causing a portion of the reference signal to be reflected by thehybrid back to the transmitting source. As a result, one cannot rely onthe hybrid to provide greater than 6 dB of attenuation in the reflectedsignal. Additionally, long range delays, such as those occurring as aresult of transmission delays, or other system effects, may presentproblems. Accordingly, it is desirable to place echo canceler 10 in thereturn loop from hybrid 12 back to the transmitting source to furtherattenuate or eliminate the affects of reflected signal r(n).

Referring to FIG. 2, a block diagram illustrating conventionalcomponents of an adaptive filter of echo canceler 10 of FIG. 1 is shown.Generally, echo canceler 10 receives reference signal x(n) and reflectedsignal r(n) and provides an error signal e(n) which is desirably zero ifthe echo canceler has accurately estimated what the reflected signalwill be.

Echo canceler 10 includes L-tap delay first-in-first-out (FIFO) memory16 for storing up to L samples of the reference signal. Echo canceler 10also includes coefficient adaptation block 17 and coefficient bank 18for storing L adaptive filter coefficients. Coefficient adaptation block17 functions to update the value of the filter coefficients after eachsample period according to well-known least-means square (LMS)calculations similar to one according to EQN. 10 which is shownhereinafter. Convolution block 20 is included for convolving the samplesin L-tap FIFO 16 with the coefficients of bank 18. The result of theconvolution represents an estimate of the reflected signal, e referencesignal, then the error signal will desirably be zero.

An echo canceler typically includes additional components including a"near-end" signal detector and a non-linear processor such as an echosuppresser or a center clipper. However, the adaptive filter is theprimary component within the echo canceler and is the subject of thepresent invention. Accordingly, it is the only component of an echocanceler that is illustrated. However, for purposes of completeness,echo canceler 10 may also include a process for monitoring the averagepower of the reference signal and the error signal such that when theadaptive filter has sufficiently converged, it enables a non-linearprocess to suppress any remaining reflected signal. Additionally, echocanceler 10 may also includes a "near-end" signal detector formonitoring the average power of the reference signal and the reflectedsignal to determine if the "near-end" signal is active whereby duringsuch period, known as double-talk for voice signal, the adaptive filtercoefficient block 17 is disabled to prevent divergence of coefficients-Also, any non-linear process would be disabled during active "near-end"signal conditions to allow the "near-end" signal to be passed.

An adaptive filter of a typical echo canceler contains L filtercoefficients as represented by h(0), h(1) . . . h(L-1). The nthcoefficient represents the finite impulse response echo model at timenT_(s), where T_(s) is the sample time. Accordingly, a conventional echocanceler can therefore model an echo impulse response with a tail lengthof time (t_(c)) as defined below in EQN. 1.

    t.sub.c =(L-1)T.sub.s                                      EQN. 1

However, a variety of circumstances, such as transmission delays andmultiple echo path, may result in echo impulse response models that falloutside this limited time range of t_(c). Additional filter taps willincrease the tail length time but such additional filter taps will alsoincrease the complexity and convergence time of the echo canceler. As aresult, because of this increase in complexity, the number of filtercoefficients, L, is typically limited in practical implementations.

Hence, a need exists for an improved echo canceler for extending thetail length time of an echo impulse response model without increasingthe number of filter taps or the complexity and convergence time of theecho canceler.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description and claims when considered inconjunction with the drawings, wherein like reference numbers refer tosimilar items throughout the drawings; and wherein

FIG. 1 is a block diagram illustrating one possible use of the placementof an echo canceler in a telephony system;

FIG. 2 is a block diagram illustrating the components of an adaptivefilter in a conventional echo canceler;

FIG. 3 is a detailed block diagram illustrating an adaptive filter foruse in an echo canceler in accordance with the present invention; and

FIGS. 4-6 are graphical diagrams showing waveforms for illustrating theoperation of the decimation echo location and partitioned echo cancelertechniques of the present invention.

DETAILED DESCRIPTION OF THE DRAWINGS

The present invention solves the problem of limited tail length time inconventional echo cancelers. Accordingly, the present invention extendsthe tail length time of an echo canceler by a factor (decimation factor)of M without increasing the real time complexity or convergence time ofthe echo canceler. In particular, the present invention performsdecimation on the reference and reflected signals to allow for passiveDecimation Echo Location (DEL) to find the high energy regions of theecho impulse response model over an extended tail length time range(t_(delpec)) as given in EQN. 2.

    t.sub.delpec =(L-1)M·T.sub.s                      EQN. 2

where M is the decimation factor

The present invention then subdivides an L-tap adaptive filter to form aPartitioned Echo Canceler (PEC) where each partition corresponds to ahigh energy region identified during the DEL mode. The resulting echocanceler retains complexity and convergent rate characteristics that areequivalent or superior than those associated with a typical L-tapadaptive filter, but the resulting echo canceler has the extended taillength time range as given in EQN. 2.

As mentioned above, the present invention enhances adaptive filtertechniques used in conventional echo cancelers by applying passiveDecimation Echo Location (DEL) training to locate high energy echo modelimpulse response regions over an extended tail length time range, asgiven in EQN. 2. The present invention then enters a partitioned echocanceler (PEC) mode which segments the adaptive filter to address thehigh energy regions identified during the DEL training mode/sequence.The present invention further models the lower energy regions of theecho model impulse response to be zero. The present invention thenaccomplishes a substantial reduction in computation and memory space byperforming convolution of only sample values of the reference signal,x(n), that occur within the identified high energy regions withcorresponding adaptive filter coefficients to arrive at an overallestimate of the reflected signal. Accordingly, the present inventionsubstantially reduces the number of required convolution multiplyaccumulate operations and coefficient update calculations as well theamount of memory required for storing adaptive filter coefficients.

Referring to FIG. 3, adaptive filter 40 is illustrated for use in anecho canceler in accordance with the present invention. Adaptive filter40 includes memory block 50 for storing the received sampled values ofthe reference signal x(n). Adaptive filter 40 also includes adaptivefilter coefficient block 56, which includes at least L elements, forstoring L filter coefficients. Memory block 50, which includes at leastML elements, may be divided into a plurality of regions whereby regions1 through N identify high energy regions associated with the echo modelimpulse response, as will be discussed in more detail below. It isunderstood that L is an integer number of coefficients in adaptivefilter block 56 and M is typically an integer number representing thedecimation factor. Non-integer M values are possible by combinationinterpolation/decimation process or other means. For purposes of thepresent invention, decimation may be more generally described as aresampling process whose final sampling period is greater than itsoriginal sampling period by a predetermined amount. Filter coefficientblock 56 is similarly divided into a plurality of regions whereby thereexists regions of block 56 corresponding to each one of the high energyecho impulse response filter coefficient regions identified during theDEL training. Adaptive filter 40 also includes convolution blocks 58₋₋ 1through 58₋₋ N for performing the necessary convolution for each of theN high energy regions, and the result of each individual convolution issummed, via adder 62, to arrive at the overall approximated reflectedsignal. The actual reflected signal is then subtracted from thisapproximated reflected signal, via adder 63 to provide the error signal,e(n), which desirably approaches zero based upon the performance ofadaptive filter 40.

Upon startup, adaptive filter 40 enters a training mode where decimationecho location (DEL) is performed. At this time, the following signalsare initialized as shown below.

Training=1

N=1

R₋₋ s(1)=0

R₋₋ e(1)=L-1

R₋₋ s₋₋ h(1)=0

R₋₋ e₋₋ h(1)=L-1

where R₋₋ s(1) and R₋₋ e(1) are used for denoting the starting andending indices of high energy region 1 within the reference memory block50, as shown in FIG. 3; and

R₋₋ s₋₋ h(1) and R₋₋ e₋₋ h(1) are the starting and ending indices forthe adaptive filter coefficients corresponding to such high energyregion 1, as shown in FIG. 3.

In the DEL training mode, the reflected signal is passed as an outputthrough multiplexer (MUX) 51 thereby avoiding sending decimated data asan output during the training mode. It is understood that in a systemcontext, it may be desirable to apply a non-linear process to eleiminatethis signal when the near-end signal is inactive.

Further, in the DEL training mode, the reference signal x(n) and thereflected signal r(n) are processed through spectral conditioners 42 and43, respectively, for extracting and positioning the necessary spectralinformation. The signals are then decimated by a factor M by decimators46 and 47. Accordingly, the adaptive filter is operating at a data rate(Fs₋₋ DEL) as given in EQN. 3 with a sample period (T_(s) ₋₋ DEL) asdefined in EQN. 4.

    Fs.sub.-- DEL=Fs/M                                         EQN. 3

    Ts.sub.-- DEL=MTs                                          EQN. 4

The advantage of training the adaptive filter at the new decimated rateis that the n-th filter coefficient now represents the finite impulseresponse echo model at time nTs₋₋ DEL. Accordingly, this extends themaximum tail length time by the decimation factor M, as shown in EQN. 2.The DEL mode trains on signals that have altered spectral components viaspectral conditioners 42 and 43 as well as decimators 46 and 47. Thespectral conditioning extracts and positions appropriate spectralregions to allow the adaptive filter to evolve in a mannerrepresentative of the results obtained if the original, non-decimatedsignals were used. Depending on the application, spectral conditioners42 and 43 may take the form of a low pass filter having a cut-offfrequency of Fs/2M. Alternately, spectral conditioners 42 and 43 mayextract and position, in the baseband, a particular frequency range orresults of a frequency sampling. Under certain conditions, the spectralconditioners could also be all-pass filters. Full convergence duringtraining is not necessary to estimate the high energy regions of thefilter, and training may be completed with a limited number ofiterations.

When the DEL training is complete, the filter coefficients represent anestimate of the impulse response echo model over the extended taillength time range of MTs.

The high energy regions of the impulse response are then determined.This can be determined by a number of different energy estimating andpeak searching techniques. For example, a technique may be utilized thatsearches for a high energy region around peak coefficient values.Alternately, a predetermined fixed number of samples positioned around apeak sample may be used to identify a high energy region.

Each of the N identified high energy regions are then mapped to theoriginal non-decimated domain to define the start and end points of eachregion whereby the notation R₋₋ s(i) and R₋₋ e(i) are used to identifythe starting and end points of high energy region i.

These points are used to select the appropriate delay regions of memoryblock 50 and are also used to calculate the filter partition pointers asshown in EQNs. 5 and 6.

    R.sub.-- s.sub.-- h(1)=0                                   EQN. 5

    R.sub.-- e.sub.-- h(1)=R.sub.-- e(1)-R.sub.-- s(1)         EQN. 6

For i=2 to N, the remaining filter partition pointers are calculated asshown in EQNs. 7 and 8 whereby the starting pointer/indices for region iis one past the ending pointer of region i-1, while the ending pointerfor region i is the starting pointer plus the size of the region.

    R.sub.-- s.sub.-- h(i)=R.sub.-- e.sub.-- h(i-1)+1          EQN. 7

    R.sub.-- e.sub.-- h(i)=R.sub.-- s.sub.-- h(i)+R.sub.-- e(i)-R.sub.-- s(i) EQN. 8

To model the extended time range as given in EQN. 2, a conventional echocanceler would need to form a convolution sum over the entire range asshown in EQN. 9. ##EQU1##

This would require a total of ML multiply accumulate operations.Similarly, ML coefficients would need to be updated with a least meansquares (LMS) calculation as shown in EQN. 10 whereby the maximum stableadaptation gain (u) decreases for increasing L, thereby limiting theconvergence rate.

    For i=0; (ML-1) h.sub.n+1 (i)=h.sub.n (i)+ue(n)x(i)        EQN. 10

However, the present invention utilizes the fact that DEL training hasidentified important high energy regions of the filter coefficientswhereby the other remaining coefficients may be modeled as zero.Therefore, memory block 50 may be partitioned, as shown in FIG. 3, intomemory elements associated with regions 1 through N. Accordingly,convolution multiply accumulate as well as coefficient update operationsassociated with the memory elements modeled as zero need not beperformed. Such a technique reduces the number of required multiplyaccumulate operations and coefficient update calculations to L_(tot), ascalculated in EQN. 11 whereby L_(tot) is essentially the sum total ofthe number of coefficients in each of the high energy regions 1 throughN. ##EQU2##

By partitioning the adaptive filter as described above, the convolutionand coefficient update equations for the PEC system may be rewritten asshown in EQNs. 12 and 13, respectively. ##EQU3##

Accordingly, the PEC system of the present invention is able to coverthe extended tail length time range given in the EQN. 2 with onlyL_(tot) convolution multiply accumulate operations and coefficientupdate calculations. In addition, the reduced number of non-zerocoefficients allow for increased adaptation gain and a subsequentdecrease in convergence time relative to a full-length conventionalapproach.

Referring to FIGS. 4-6, waveforms for illustrating an example of theoperation of the Decimated Echo Location (DEL) and Partitioned EchoCanceler (PEC) techniques are shown. In this example, the number ofcoefficients, L, is fixed at 512. A typical application might have asample rate of Fs=8000 Hz with a corresponding Ts=0.125 milliseconds(ms). Therefore, a conventional echo canceler could model an echoimpulse response over a range of approximately 64 ms, as calculatedaccording to EQN. 1. However, through the use of DEL/PEC techniques witha decimation factor (M) of 5, we can model echo over the extended rangeof approximately 320 ms, as calculated according to EQN. 2.

FIG. 4 illustrates the true (non-decimated) impulse response adaptivefilter coefficients. As shown, a conventional approach would requireover 2000 taps to model this response. However, as is often the case,the important high energy regions of the impulse response areconcentrated over limited region(s). In this example, there are clearlyN=2 important regions in the impulse response as indicated by Region 1and Region 2 of FIG. 4 with respective peak values 101 and 103 occurringat n1=100 and n1=2000. These peak values correspond to times 12.5 ms and250 ms. Through the use of DEL training, however, these importantregions can be identified with a significantly reduced number of filtertaps. Based on the results of the DEL training, partitioning of the echocanceler filter is performed to address only these regions of interest,thereby significantly increasing the range and efficiency of the fixednumber of filter taps available.

FIG. 5 illustrates the impulse response obtained using decimated dataduring the DEL training mode in accordance with the present invention.Although only 512 taps are used, decimating by a factor of M=5 allowsthe impulse responses to be modeled over the extended range ofapproximately 320 ms. FIG. 4 is not the true impulse response given thatthe reference and reflected signals have been modified. However, asshown in the similarities between FIG. 4 and FIG. 5, proper spectralconditioning prior to decimation allows for the decimated model toevolve in a manner representative of the true impulse response.

The impulse response model created during the DEL training mode againclearly shows two important regions as represented by Region 1 andRegion 2 of FIG. 5. By using a combination of peak searches, fixedwindowing, or variable window sizes based on energy region calculations,the region boundaries can be determined. FIG. 5 shows that Region 1 hasa peak value 105 occurring at n2=20 and extends primarily over the(decimated) coefficient range of 0 to 40, and Region 2 has a peak value107 occurring at n2=400 and extends over the (decimated) coefficientrange 380 to 420.

Given that the impulse response of FIG. 5 was generated from decimateddata, the identified high energy regions must be mapped to the original(non-decimated) domain. Therefore the true (non-decimated) range ofRegion 1 is 0 to 200, and Region 2 is 1900 to 2100. These rangescorrespond with our initial visual inspection of FIG. 4, and illustratehow DEL training can be used to identify regions of interest over anextended range.

The results of the DEL training and the identification of these highenergy regions can now be used to partition the echo canceler. In FIG.3, the reference memory block 50 operates as a FIFO buffer. Therefore,the entire extended range (ML) must be maintained, although at any pointin time only the data in the regions of interest will be utilized.Therefore we can calculate the reference data indices directly from theregion boundaries. Continuing with our example, the starting and endingpoints for the regions 1 and 2 are as follows.

R₋₋ s(1)=0

R₋₋ e(1)=200

R₋₋ s(2)=1900

R₋₋ e(2)=2100

Operating the filter in a partitioned mode assumes that coefficientsoutside the regions of interest are to be modeled as zero, andtherefore, do not contribute to the echo cancellation. As such, it isonly necessary to maintain L elements as shown in the FIG. 3 filtercoefficient memory block 56.

FIG. 6 illustrates how the partitioned filter coefficient memory blockcontains several sections, each one modeling an important region of theimpulse response. Basically, the partitioned coefficient memory blockcontains the coefficients associated with each high energy regiongrouped serially and adjacent to each other, as shown in FIG. 6. Allintermediate coefficients are modeled as zero and, thus, may bediscarded. The indices used to access the coefficient memory block canbe calculated in a straightforward manner, as given in EQNs. 5-8,whereby reference letters A and B denote the starting and ending indicesfor high energy region block 1 and reference letters C and D denote thestarting and ending indices for high energy region block 2.

A=R₋₋ s₋₋ h(1)=0

B=R₋₋ e₋₋ h(1)=(200-0)=200

C=R₋₋ s₋₋ h(2)=200+1=201

D=R₋₋ e₋₋ h(2)=201+(2100-1900)=401

These coefficients, along with their respective x(n) sample counterpartsstored in reference memory block 50, are the only ones that are used toconvolve the reference signal, x(n), with the impulse response, h(n), togenerate the estimated reflected signal ulate operations and coefficientupdate calculations to L_(tot), as calculated in EQN. 11.

By now it should be apparent that the present invention has provided anovel adaptive filter within an echo canceler. The adaptive filteroperates in a first mode for performing decimation echo location (DEL).During the DEL mode, echo cancellation is modeled over an extended rangeusing decimated data, and then high energy regions occurring within theecho impulse response are identified. The adaptive filter then operatesin a second mode for performing partitioned echo cancellation (PEC).During the PEC mode, the identified high energy region filtercoefficients of the impulse response are partitioned and convolution ofthe reference signal with the impulse response signal is accomplished byperforming convolution on only those samples in the reference signalmemory block that correspond to respective high energy region filtercoefficients in the impulse response signal. Filter coefficients notassociated with these high energy regions are modeled as zero and, thus,any associated calculations therewith may be neglected. Accordingly, theadaptive filter of the present invention extends the tail length timerange of conventional adaptive filters while not increasing the numberof required filter coefficient taps and substantially reduces the numberof convolution operations and coefficient update calculations.

While the invention has been described in conjunction with specificembodiments thereof, many alternatives, modifications and variationswill be apparent to those of ordinary skill in the art in light of theforegoing description. Accordingly, the invention is intended to embraceall such alternatives, modifications and variations as fall within thebroad scope of the appended claims.

We claim:
 1. A method for performing echo cancellation whereby echodenotes a portion of a reference signal appearing on an output signalpath being coupled back to an input signal path thereby generating areflected signal and resulting in echo, the method comprising the stepsof:entering a training mode comprising the steps of:performing spectralconditioning on each incoming sample of both the reference and reflectedsignals; decimating both the reference and reflected signal therebyextending the time range that can be modeled by the adaptive filter;allowing the adaptive filter to operate on the decimated reference andreflected signals thereby obtaining a model representative of an echoimpulse response over the time range; identifying high energy regionswithin the echo impulse response; mapping the high energy regions of theecho impulse response in the decimated domain to corresponding regionsin the non-decimated domain; partitioning respective samples of thereference signal that correspond to the high energy regions of theimpulse response signal; exiting the training mode and entering apartitioned mode, comprising the step of:performing convolution on onlysamples of the reference signal and the echo impulse response thatcorrespond to the high energy regions of the echo impulse response togenerate an estimated reflected signal and thereby reduce the number ofcoefficient taps required to perform echo cancellation over the timerange and reduce the number of convolution multiply accumulateoperations required for convoluting the reference signal with thereflected signal.
 2. The method of claim 1 further including the step ofperforming adaptive filter coefficient updates on only corresponding tothe high energy regions thereby reducing a total number of coefficientupdate calculations required.
 3. The method of claim 1 further includingthe step of subtracting said estimated reflected signal from thereflected signal to generate an error signal which desirably approacheszero.
 4. An adaptive filter for use in performing echo cancellationfunctions, the adaptive filter being responsive to a reference signalappearing on a signal path and a reflected signal whereby the reflectedsignal represents a portion of the reference signal that is reflectedback onto the signal path thereby generating the reflected signal andresulting in echo, the adaptive filter functioning to make the reflectedsignal substantially equal to zero, the adaptive filter comprising:areference signal memory block for storing samples of the referencesignal; an adaptive filter coefficient block for storing adaptive filtercoefficients; a first decimator, responsive to the reference signal, forsupplying a decimated version of the reference signal; a firstmultiplexer having an output for alternately supplying the referencesignal and the decimated version of the reference signal to thereference signal memory block; a plurality of convolution circuits forperforming convolution of the samples associated with high energyregions of the echo impulse response with corresponding samples of thereference signal; a first summer for summing the result of each of theplurality of convolution circuits thereby providing an outputrepresenting an estimate of the reflected signal a second decimator,responsive to the reflected signal, for supplying a decimated version ofthe reflected signal; a second multiplexer having an output foralternately supplying the reflected signal and the decimated version ofthe reflected signal to an output of the second multiplexer; a secondsummer for subtracting the estimate of the reflected signal appearing atthe output of the first summer from the output of the second multiplexerthereby providing an output representing an error signal; wherein theadaptive filter operates in a first mode whereby the first multiplexerprovides the decimated version of the reference signal to the referencesignal memory block and whereby the second multiplexer provides thedecimated version of the reflected signal to the second summer and theadaptive filter operates on the decimated versions to obtain an echoimpulse response thereby extending the time range of the echo impulseresponse that can be modeled by the adaptive filter; wherein high energyregions of the echo impulse response and corresponding samples withinthe reference signal memory block are identified; and wherein theadaptive filter operates in a second mode whereby echo cancellationfunctions are implemented by performing convolution on only samples ofthe reference signal and the echo impulse response reflected signal thatcorrespond to the high energy regions of the echo impulse responsethereby reducing the number of coefficient taps required to perform echocancellation over the time range and reducing the number of multiplyaccumulate operations required for convolving the reference signal withthe echo impulse response and thereby reducing the number of coefficientupdate calculations.
 5. The adaptive filter of claim 4 further includinga third multiplexer for alternatley supplying the reflected siganl orthe output of the second summer to an output of the adaptive filter. 6.The adaptive filter of claim 4 further including a coefficientadaptation block, coupled between the output of the second summer andthe adaptive filter coefficient block for updating the values of theadaptive filter coefficients.
 7. The adaptive filter of claim 4 furtherincludinga first spectral conditioner for processing the referencesignal, said first spectral conditioner having an input coupled forreceiving the reference signal and an output coupled to the firstdecimator; and a second spectral conditioner for processing thereflected signal, said second spectral conditioner having an inputcoupled for receiving the reflected signal and an output coupled to thesecond decimator.
 8. A method for extending the time range of an impulseresponse of an adaptive filter during a training sequence for thepurpose of identifying high energy regions of an impulse response overan extended time range, the adaptive filter being responsive to firstand second signals, the method comprising the steps of:entering atraining sequence for:performing spectral conditioning on each incomingsample of both the first and second signals whereby said spectralconditioning alters a power density of said first and second signals foridentifying high energy regions; decimating both the first and secondsignals by a predetermined factor; and allowing the adaptive filter tooperate on the decimated first and second signals thereby obtaining animpulse response for the adaptive filter over a time range that islonger in time, by the predetermined factor, than a corresponding timerange if the adaptive filter were operating on non-decimated versions ofthe first and second signals.